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Accurate parameters are crucial in modern energy systems to ensure the reliable operation of all components. Given the substantial volume of data in monitored systems, high-performance methods are necessary. This paper proposes a new Bayesian multi-output regressor for estimating the parameters of a three-phase transmission line. The presented approach achieves acceptable accuracy in parameter estimation using only one end of the line. The Bayesian regressor is developed using information derived from the data themselves, eliminating the need to explicitly model the system. This capability allows the method to estimate parameters while accommodating different noise models, even in the presence of systematic errors and non-Gaussian random noise. The methodology was validated on various systems, including a two-bus system, IEEE 14-bus, IEEE 39-bus, and IEEE 118-bus, under diverse conditions such as varying sample sizes, loads, and noise levels. These tests demonstrate the robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events, causing surveillance and energy loss. This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters. Initially, the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes. Subsequently, it delves into communication and data processing mechanisms, specifically focusing on central data processing (CDP), communication routers (CRs), and phasor measurement units (PMUs), to re-establish an equipment recovery model based on these data transmission methodologies. Furthermore, it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution. The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system. The findings suggest that the proposed branch- and-bound algorithm significantly augments the observational capabilities of a power system with limited resources, thereby bolstering its stability and emergency response mechanisms.

Methods for short-term voltage stability (STVS) assessment based on phasor measurement unit (PMU) data have become quite abundant. However, most of them are challenged to deal with the commonly encountered incomplete PMU data. Existing models for STVS assessment of incomplete PMU data mostly have the disadvantages of high computational complexity and large impact from the level of data missing. This paper proposes a multi-task learning method that performs stability assessment and missing data completion in parallel. The Transformer Encoder is used as the shared feature extractor, a GRU structure is used to output complete PMU data sequences, and a BP neural network is used to output stability assessment results. The loss function used for the missing completion task considers the accuracy in both the shape and time domains. A comparison with relevant models shows that the model proposed in this paper achieves the best performance in all indicators and is minimally affected by the observation windows and the level of data loss, demonstrating good robustness. [ABSTRACT FROM AUTHOR]

With the large-scale use of power electronic devices, the alternating current side of the system produces decaying direct current components, greatly reducing the performance of the phasor estimation algorithms. To address this problem, this paper presents a phasor estimation algorithm based on sinc interpolation. First, an equivalent cosine model is proposed to simulate the dynamic characteristics of the decaying direct current component. Second, based on the boundary conditions, the optimal frequency band of the equivalent cosine model is determined iteratively to reconstruct the decaying direct current component. Last, the phasor form of the components to be measured is modeled via sinc interpolation, and the phasor parameters are obtained via least squares. In the simulation, the accuracy of the equivalent cosine model is improved by 319.56% compared with that of the traditional method. The proposed algorithm satisfies the accuracy requirement of 1% total vector error in the protection class phasor measurement unit framework.

Microgrids are a viable approach for improving energy resiliency and sustainability. Current microgrid systems frequently suffer from inadequate fault identification and compensation techniques, resulting in operational inefficiencies and grid instability. Conventional control strategies may not effectively manage changing environmental conditions and variations in load, requiring a more adaptable and intelligent approach. The paper presents a sophisticated method that combines Fuzzy Logic Controllers (FLCs) with Synchrophasor Technology (ST) to tackle these challenges. This work aims to develop and execute a thorough solution for microgrids’ fault identification, compensation, and power management. The paper proposed a MATLAB/Simulink model that includes a Solar Photovoltaic (SPV) system with an FLC-based Maximum Power Point Tracking (MPPT), a Diesel Generator (DG), a Battery Energy Storage System (BESS) with an FLC-based Battery Management System (BMS), a Voltage Source Inverter (VSI), and a Unified Power Flow Controller (UPFC) with a FLC. Phasor Measurement Units (PMUs) equipped with ST are used to monitor and identify the anomalies in the system. The analysis of the simulation data provides evidence of the efficacy of the suggested approach. The SPV system demonstrates strong performance in producing voltage and current, successfully adjusting to different irradiance levels. The MPPT algorithm based on FLC guarantees the highest power efficiency. Additionally, the BESS and UPFC, controlled by FLCs, enhance grid stability and power quality. By integrating with synchrophasor-enabled PMUs, the microgrid may achieve accurate fault detection, and thus, the operator can take corrective actions to guarantee dependable operation even under challenging circumstances. Furthermore, the reliability and efficiency of the proposed system were confirmed using a Real-Time Digital Simulator (RTDS) using RSCAD with PMUs in real-world environments. By employing hardware-based PMUs, the system can accurately monitor grid interactions in real-time, enabling prompt detection of disruptions and proactive deployment of control measures. The RTDS results showcased the system’s ability to maintain grid stability and efficiently manage faults, as evidenced by the precise current and voltage magnitudes measurements and their phase angles, frequency, and Rate of Change of Frequency (ROCOF) during fault scenarios.

Concerns have been voiced about the growing significance of cyber-threats, especially in light of the potentially dire repercussions of false data injection (FDI) assaults. This work investigates FDI detection in phasor measurement units (PMU), focusing on instances where an attack can be launched simply by compromising one unit. Simulated post-processing adversarial interventions i.e., noise and non-linearity were introduced to train and fortify the system against possible attacks and to render it resilient to perturbations. By learning complex non-linear patterns from the data, a deep de-noising auto-encoder model is used to de-noise and learn genuine feature representations, improving overall reliability. The suggested framework performs better than conventional machine learning and 1-D CNN models when it comes to precisely estimating intrusion, as shown by a comparison study. By using an integrated strategy, power system monitoring and control become more accurate and resilient, successfully tackling the changing issues faced by contemporary electrical grids. The proposed adversarially robust framework is evaluated using Monte-Carlos simulations and on varying load conditions to better comprehend the impact of adversarial interventions on the FDI detection accuracy under different load characteristics and attack scenarios. The proposed framework yielded an average 98.3% in Monte Carlo simulations and an average of 96.5% accuracy under varying load conditions. Surpassing the conventional ML and 1-D CNN algorithms in successfully identifying FDI attacks under adversarial vulnerability.

The increasing penetration of renewable energy resources with highly fluctuating outputs has placed increasing concern on the accuracy and timeliness of electric power system state estimation (SE). Meanwhile, we note that only a fraction of system states fluctuate at the millisecond level and require to be updated. As such, refreshing only those states with significant variation would enhance the computational efficiency of SE and make fast-continuous update of states possible. However, this is difficult to achieve with conventional SE methods, which generally refresh states of the entire system every 4–5 s. In this context, we propose a local hybrid linear SE framework using stream processing, in which synchronized measurements received from phasor measurement units (PMUs), and trigger/timing-mode measurements received from remote terminal units (RTUs) are used to update the associated local states. Moreover, the measurement update process efficiency and timeliness are enhanced by proposing a trigger measurement-based fast dynamic partitioning algorithm for determining the areas of the system with states requiring recalculation. In particular, non-iterative hybrid linear formulations with both RTUs and PMUs are employed to solve the local SE problem. The timeliness, accuracy, and computational efficiency of the proposed method are demonstrated by extensive simulations based on IEEE 118-, 300-, and 2383- bus systems.

Vulnerability assessment forms the foundation for remediation activities that an organization develops as a part of risk assessment and mitigation. This paper presents a synchrophasor measurement device-specific cyber security vulnerability assessment at the network level. A synchrophasor network is essential for a wide-area measurement system (WAMS) that assembles time-synchronized data from multiple power network components and facilitates the processing and transferring of these data. Attackers can leverage their knowledge of firmware and protocols to disrupt or critically damage the functioning of the power system stealthily. Nevertheless, the exploitation can be curbed or controlled if the operator is aware of the vulnerabilities associated with the field devices. The authors detail their analysis on the following aspects: 1) classification of attacks in synchrophasor network; 2) type of resources and reconnaissance sufficient to launch an attack on field devices; and 3) identification, demonstration, and exploitation of synchrophasor device vulnerabilities. The work showcases exploitable vulnerabilities to understand the breadth and scope of a synchrophasor measurement device’s exposure to a possible cyber attack. The reverse-engineered attack focuses on capturing essential features of packets and consuming the bandwidth during the three-way handshake among legitimate entities. The latency in packet transmission is gradually increased, which results in retransmission; thus, the legitimate connection gets terminated. Thus, the paper provides situational awareness at the device level and credible information regarding loopholes and weak links in field devices.

This study aims to perform fast fault diagnosis and intelligent protection in an active distribution network (ADN) with high renewable energy penetration. Several time-domain simulations are carried out in EMTP-RV to extract time-synchronized current and voltage data. The Stockwell transform (ST) was used in MATLAB/SIMULINK to preprocess these input datasets to train the adaptive fault diagnosis deep convolutional neural network (AFDDCNN) for fault location identification, fault type identification, and fault phase-detection for different penetration levels. Based on the AFDDCNN output, the intelligent protection scheme (IDOCPS) generates the signal for isolating a faulty section of the ADN. An intelligent fault diagnosis scheme that combines ST and deep learning methods aids the artificial intelligence-based protection scheme in isolating the faulty section. This study uses the PyTorch framework to build both the AFDDCNN and IDOCPS. The proposed protection technique classifies and isolates faults and coordinates protection with minimum operating time in the IEEE 13-bus ADN. It consistently gives high accuracy for fault diagnosis and minimum operating time for the IDOCPS even when the network's topology is modified to the IEEE 34-bus ADN. The experimental results indicate that the proposed model is more accurate and provides faster fault diagnosis and isolation than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research. [ABSTRACT FROM AUTHOR]

Abstract Methods for short-term voltage stability (STVS) assessment based on phasor measurement unit (PMU) data have become quite abundant. However, most of them are challenged to deal with the commonly encountered incomplete PMU data. Existing models for STVS assessment of incomplete PMU data mostly have the disadvantages of high computational complexity and large impact from the level of data missing. This paper proposes a multi-task learning method that performs stability assessment and missing data completion in parallel. The Transformer Encoder is used as the shared feature extractor, a GRU structure is used to output complete PMU data sequences, and a BP neural network is used to output stability assessment results. The loss function used for the missing completion task considers the accuracy in both the shape and time domains. A comparison with relevant models shows that the model proposed in this paper achieves the best performance in all indicators and is minimally affected by the observation windows and the level of data loss, demonstrating good robustness.

Accurate parameters are crucial in modern energy systems to ensure the reliable operation of all components. Given the substantial volume of data in monitored systems, high-performance methods are necessary. This paper proposes a new Bayesian multi-output regressor for estimating the parameters of a three-phase transmission line. The presented approach achieves acceptable accuracy in parameter estimation using only one end of the line. The Bayesian regressor is developed using information derived from the data themselves, eliminating the need to explicitly model the system. This capability allows the method to estimate parameters while accommodating different noise models, even in the presence of systematic errors and non-Gaussian random noise. The methodology was validated on various systems, including a two-bus system, IEEE 14-bus, IEEE 39-bus, and IEEE 118-bus, under diverse conditions such as varying sample sizes, loads, and noise levels. These tests demonstrate the robustness of the proposed approach.

Modern electrical power systems place special demands on the speed and accuracy of transient and steady-state process control. The introduction of renewable energy sources has significantly influenced the amount of inertia and uncertainty of transient processes occurring in energy systems. These changes have led to the need to clarify the existing principles for the implementation of devices for protecting power systems from the loss of small-signal and transient stability. Traditional methods of developing these devices do not provide the required adaptability due to the need to specify a list of accidents to be considered. Therefore, there is a clear need to develop fundamentally new devices for the emergency control of power system modes based on adaptive algorithms. This work proposes to develop emergency control methods based on the use of deep machine learning algorithms and obtained data from synchronized vector measurement devices. This approach makes it possible to ensure adaptability and high performance when choosing control actions. Recurrent neural networks, long short-term memory networks, restricted Boltzmann machines, and self-organizing maps were selected as deep learning algorithms. Testing was performed by using IEEE14, IEEE24, and IEEE39 power system models. Two data samples were considered: with and without data from synchronized vector measurement devices. The highest accuracy of classification of the control actions' value corresponds to the long short-term memory networks algorithm: the value of the accuracy factor was 94.31% without taking into account the data from the synchronized vector measurement devices and 94.45% when considering this data. The obtained results confirm the possibility of using deep learning algorithms to build an adaptive emergency control system for power systems. [ABSTRACT FROM AUTHOR]

This paper presents a method for stability improvement for the standard IEEE14 bus system by using Static VAR Compensators (SVCs). The SVC, a shunt Flexible AC Transmission System (FACTS) device, can effectively improve the voltage profile at several buses in the system, thereby improving the overall system stability. In this system, some loads are increased by either (1) switching on a sudden load change parallel to the original loads in the system or (2) scaling the original load by a large factor. The resulting system is an overloaded version of the IEEE14 bus system. A Phasor Measurement Unit (PMU) is installed at each bus in the system to measure accurately the voltage magnitude, the frequency, and the phase angle values in every bus location. This study compares the voltage magnitude and frequency for four cases. In each case, the SVC is installed in a different location in the system. The voltage magnitude values at each bus are recorded before switching on the additional loads, during the period when the additional loads are switched on, and after switching off the additional loads. Each case has been studied separately, and the SVC parameters have been adjusted separately for each case to provide a suitable voltage compensation, while maintaining stable operation. The incorporation of SVCs has shown improvements and advantages in the system's voltage and transient stability. In addition, the overshoot and the settling time of the voltage magnitude and frequency have been recorded to study further the stability of these signals. The best results for voltage magnitude compensation have been shown to occur at the bus where the SVC is installed. The best results in terms of improvement in voltage magnitude have been achieved when the SVC has been installed at bus 3, while the best results in terms of improvements in overshoot for voltage and frequency have been obtained when the SVC has been installed at bus 6. [ABSTRACT FROM AUTHOR]

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To address phasor measurement unit (PMU) vulnerability to spoofing, we propose the use of a set-valued state estimation technique known as stochastic reachability (SR)-based distributed Kalman filter (DKF) that computes secure global positioning system (GPS) timing across a network of receivers. Utilizing SR, we estimate not only GPS time but also its stochastic reachable set, which is parameterized by probabilistic zonotope (p-Zonotope). While requiring known measurement error bounds in only non-spoofed conditions, we designed a two-tiered approach. We first performed measurement-level spoofing mitigation via deviation of a measurement innovation from its expected p-Zonotope. We then performed state-level timing risk analysis via a determination of the intersection probability of the estimated p-Zonotope with an unsafe set that violates IEEE C37.118.1a-2014 standards. Finally, we validated our SR-DKF algorithm by subjecting it to a simulated receiver network to coordinate signal-level spoofing. We demonstrate improved timing accuracy and successful spoofing mitigation via the use of our SR-DKF algorithm. We also validated the robustness of the estimated timing risk as the number of receivers were varied. [ABSTRACT FROM AUTHOR]

The increasing consumption of the electric energy aimed at develop the transmission networks and the demand for higher reliability from the network; in this regard, wide-area measurement systems using phasor measurement units (PMUs) have revolved the trend of power network management. In this paper, the optimal allocation of PMUs in order to reach the perfect observability of the network; based on a differential evolution algorithm, is proposed and it is shown that, the deployment of constraints related to the zero-injection busses (ZIB) aimed to decrease the number of PMUs and their corresponding cost. By comparing the proposed method to the other methods, its simplicity and good performance are approved. [ABSTRACT FROM AUTHOR]

The rapid growth of electric vehicle (EV) penetration has led to more flexible and reliable vehicle-to-grid-enabled cyber-physical systems (V2G-CPSs). However, the increasing system complexity also makes them more vulnerable to cyber-physical threats. Coordinated cyber attacks (CCAs) have emerged as a major concern, requiring effective detection and mitigation strategies within V2G-CPSs. Digital twin (DT) technologies have shown promise in mitigating system complexity and providing diverse functionalities for complex tasks such as system monitoring, analysis, and optimal control. This paper presents a resilient and secure framework for CCA detection and mitigation in V2G-CPSs, leveraging a smart DT-enabled approach. The framework introduces a smarter DT orchestrator that utilizes long short-term memory (LSTM) based actor-critic deep reinforcement learning (LSTM-DRL) in the DT virtual replica. The LSTM algorithm estimates the system states, which are then used by the DRL network to detect CCAs and take appropriate actions to minimize their impact. To validate the effectiveness and practicality of the proposed smart DT framework, case studies are conducted on an IEEE 30 bus system-based V2G-CPS, considering different CCA types such as malicious V2G node or control command attacks. The results demonstrate that the framework is capable of accurately estimating system states, detecting various CCAs, and mitigating the impact of attacks within 5 seconds. [ABSTRACT FROM AUTHOR]

In this paper, the performance limits of faults localization are investigated using synchrophasor data. The focus is on a non-trivial operating regime where the number of Phasor Measurement Unit (PMU) sensors available is insufficient to have full observability of the grid state. Proposed analysis uses the Kullback Leibler (KL) divergence between the distributions corresponding to different fault location hypotheses associated with the observation model. This analysis shows that the most likely locations are concentrated in clusters of buses more tightly connected to the actual fault site akin to graph communities. Consequently, a PMU placement strategy is derived that achieves a near-optimal resolution for localizing faults for a given number of sensors. The problem is also analyzed from the perspective of sampling a graph signal, and how the placement of the PMUs i.e. the spatial sampling pattern and the topological characteristic of the grid affect the ability to successfully localize faults. To highlight the superior performance of presented fault localization and placement algorithms, the proposed strategy is applied to a modified IEEE 34, IEEE-123 bus test cases and to data from a real distribution grid. Additionally, the detection of cyber-physical attacks is also examined where PMU data and relevant Supervisory Control and Data Acquisition (SCADA) network traffic information are compared to determine if a network breach has affected the integrity of the system information and/or operations.

The lifespan of overhead transmission conductors is influenced by various factors, including design, maintenance, and operating conditions. Prolonged exposure to high temperatures, particularly near maximum design values, can significantly age transmission lines (TLs). The dynamic thermal rating (DTR) system, an advanced technology that increases TL capacities, can contribute to these stringent conditions. DTR overlays a cyber layer with communication and control systems onto the physical network, transforming it into a cyber-physical network. Currently, the increased risk of TL ageing associated with DTR, and the cyber layer’s functionality lack a unified framework for quantification. This study introduces a novel framework that considers the cyber layer’s network topology and its impact on TL ageing due to DTR implementation. It also proposes the implementation of system integrity protection schemes (SIPS) within the framework. SIPS detects abnormal conditions from DTR deployment, like excessive line loading, and takes corrective actions to optimize DTR system’s performance. The study employs a Sequential Monte Carlo Simulation (SMCS) technique on a modified cyber-physical IEEE RTS-79. The study reveals that topologies like double ring and mesh, cause higher line ageing indices compared to line and star topologies. ETNA values remain relatively stable across the network topologies within a year, suggesting minimal short-term impact on transmission line ageing. However, cumulative effects over several years can potentially reduce lifespan. SIPS can slow network ageing by up to 5hr/yr, but their contingencies can cause the opposite effect. Nonetheless, a reliable SIPS can maintain an ETNA value of 101hr/yr without increasing load curtailment.

This paper introduces a framework for online identification of cascading events in power systems with renewable generation, based on supervised machine learning techniques and measurement data. Cascading events are low-probability, high-impact events, the propagation of which can lead even to large-scale blackouts, with severe consequences to society. The proposed methodology is based on Long-short term memory networks, considering uncertainties associated with renewable generation, system loading and initial contingencies. By utilizing time-series measurement data, the proposed method can predict the appearance of cascading events, as defined by the discrete action of protection devices which can capture voltage, frequency or transient instability related dynamic phenomena. The proposed framework is applied on a modified version of the IEEE-39 bus model incorporating detailed dynamic renewable generation and protection devices implementations. Results highlight that the suggested method can successfully identify cases with cascading events with up to 95.6% accuracy and with an average inference time of 0.042s, taking into account practical considerations related to phasor measurement units, such as availability and noise in measurement data.

Due to low levels of observability and automation in active distribution networks (ADNs), the deployment of accurate measurement devices would be inevitable to increase the network observability. This work develops an optimal framework for phasor measurement unit (PMU) placement considering the accuracy of the distribution system state estimation (DSSE) process. In this regard, first, considering the significance of active power in supplying network loads, an active power sensitivity analysis is conducted in which the accuracy of active power injection at each bus is multiplied by its value of lost load. In this way, the accuracy of active power estimation could be transformed into a monetary value. Then, based on the determined sensitivity criterion, the optimal placement of PMUs has been performed with the aim of appropriate accuracy for the DSSE. On the other hand, the accuracy of the results obtained in the state estimation procedure can affect the estimation of line-loadings and lead to load shedding due to the low accuracy of estimated line-loadings. It is noteworthy that due to the high integration of distributed energy resources in distribution systems, ADNs would be prone to congestion issues. Therefore, in the next stage, this perspective is also used for determining the optimal number and location of PMUs in ADNs to improve their observability based on the operational conditions of the network. Finally, the developed algorithm is applied on the 77-bus-UK-test distribution network to investigate its effectiveness in improving the DSSE accuracy and mitigating interrupted loads. The numerical results indicate that implementing the proposed framework not only increases the DSSE accuracy but also decreases the cost of compensating the accuracy of active power by 7% compared to the typical network without PMUs. Further, the results show that the proposed model significantly mitigates the total curtailed loads due to the low accuracy of estimated line-loadings.

Electric power system operators monitor large multi-modal data streams from wide service areas. The current data setups stand to get more complex as utilities add more smart-grid sensors to collect additional data from power system substations and other in-situ locations. We propose a methodology to utilize multi-modal data for automated power system fault prediction, and precursor discovery that takes advantage of not only the utility owned measurements but also an abundance of data from other related databases such as weather observation systems. The process is automated to help operators analyze multi-modal data that may be impossible to process manually due to the size and variety. We automatically preprocess multi-source data and learn a joint latent representation from collocated streamed, sparse, and high-dimensional data collected from Phasor Measurement Units and external weather data. Then we utilize multi-instance learning to predict events and discover precursors simultaneously without relying on post-mortem studies of fault signatures. We apply the proposed methodology to provide early predictions of faults in the U.S. Western Interconnection. AU-ROC of 0.94 is achieved in predicting events by utilizing information 5 hours before event time using season-specific models. We show how precursors can be extracted from multi-modal data and interpreted for predicted events.

The impact of false data injection (FDI) attacks on static state estimation of power systems has been actively studied in the past decade. In this paper, we consider an estimation method that first decomposes the system into islands and then implements robust regression estimators at the island level as well as the system level. We carry out an analysis to establish its advantages in terms of state estimation accuracy and attack detections. In particular, we focus on highly adversarial cases where the attacker can attack both the measurement vector and the regressor matrix and attempts to manipulate the states to targeted values. Our estimation approach employs a system decomposition method capable to generate islands small in their sizes and applies the robust estimation method of least trimmed squares. We make comparisons with methods using other decompositions and other robust estimators. To this end, we analyze the structure of the system topology and measurements and perform extensive simulations using the IEEE 14- and 118-bus systems. Furthermore, we investigate robustness improvement when phasor measurement units (PMUs) are available and hybrid state estimation can be employed.

Phasor measurement unit (PMU) is deemed a pivotal tool in wide-area monitoring, protection, and control of power systems. The optimal allocation of PMUs has recently received more attention. In this paper, Binary Integer Programming (BIP) technique is proposed to optimally allocate PMUs in the power network keeping complete observability of the network and reducing the total numbers of PMUs. The proposed technique is tested and examined through different systems to investigate the effectiveness of BIP. Additionally, the BIP is compared with different techniques to verify its superiority and reveal the main contributions of BIP-based algorithms using different IEEE test systems. The proposed method shows an efficient technique while placing PMUs through direct, simple, and linear formulas regardless of the network scale or configuration; transmission, or distribution systems. Moreover, the whole process consumes the least time slots to hit the intended goal because of the problem formulating criteria.

The reactive nature of traditional under-frequency load shedding schemes can lead to delayed response and unnecessary loss of load. This work presents a proactive framework for power system frequency stability. Bayesian filters and synchrophasors are leveraged to produce predictions after disturbances are detected. By being able to estimate the future state of frequency corrective actions can be taken before the system reaches a critical condition. This proactive approach makes it possible to optimize the response to a disturbance, which results in a decrease in the amount of compensation utilized. The framework is tested via Matlab simulations based on Kundur's Two-Area System, and the IEEE 14-Bus System. Performance metrics are provided and evaluated against other contemporary solutions found in literature. During testing this framework outperformed other solutions by drastically reducing the amount of load dropped during compensation. [ABSTRACT FROM AUTHOR]

Synchronized phasor measurement systems are being widely used around the world and have become essential elements in the evolution of the operation of large electrical power systems (EPS). These systems, called Phasor Measurement Units (PMUs), are capable of recording and communicating dynamic data from the EPSs in a synchronized way by GPS and with a high sampling rate, generate a huge set of data that, among many applications, has the capacity to detect events. In this way, this work presents a data management system architecture applied to a real PMU system located in the state of Paraná, Brazil that detects and storages events using principal component analysis and Pearson correlation. This method can detect and store electrical events that occurred during the operation of the national interconnected system of Brazil with good results. [ABSTRACT FROM AUTHOR]

Smart Grids (SG) is a power system development concept that has received significant attention nationally. SG signifies real-time data for specific communication requirements. The best capabilities for monitoring and controlling the grid are essential to system stability. One of the most critical needs for smartgrid execution is fast, precise, and economically synchronized measurements, which are made feasible by Phasor Measurement Units (PMU). PMUs can provide synchronized measurements and measure voltages as well as current phasors dynamically. PMUs utilize GPS time-stamping at Coordinated Universal Time (UTC) to capture electric phasors with great accuracy and precision. This research tends to Deep Learning (DL) advances to design a Residual Network (ResNet) model that can accurately identify and classify defects in grid-connected systems. As part of fault detection and probe, the proposed strategy uses a ResNet-50 technique to evaluate real-time measurement data from geographically scattered PMUs. As a result of its excellent signal classification efficiency and ability to extract high-quality signal features, its fault diagnosis performance is excellent. Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time. The proposed approaches classify the fault type with a precision of 98.5% and an accuracy of 99.1%. The long-short-term memory (LSTM), Convolutional Neural Network (CNN), and CNN-LSTM algorithms are applied to compare the networks. Real-world data tends to evaluate these networks. [ABSTRACT FROM AUTHOR]

The fractional open-circuit voltage (FOCV) method is extensively used in low-power energy harvesting (EH) sources to extract maximum power. For fast-varying EH sources, a fast sampling rate is required. This work theoretically analyzes the influence of the sampling time and period on the harvested power of sinusoidal EH sources. In addition, the circuit limitations to achieve a fast sampling rate are presented and circuits to deal with them proposed and implemented. Furthermore, one of the circuits is based on a novel pseudoFOCV method and achieves the fastest sampling rate. Experimental tests are performed with a 2 Hz, 1 to 3 V sinusoidal source having an output resistance of 127 Ω, and the results are shown to agree with theoretical predictions. It is shown that the harvested power increases with the sampling rate when the sampling time is negligible (sampling 15 times faster than the source frequency extracts around 99% of the maximum), and for fixed sampling times, there is an optimum sampling rate where the harvested power is maximum. The first result is generic and valid for methods other than the FOCV. Tests were also performed with a small-scale wave energy converter placed in a linear shaker emulating a sea environment. Harvested power increases by 25% with respect using a commercial FOCV unit with a low sampling rate. [ABSTRACT FROM AUTHOR]

• Discussion on the advanced local protection against the stressed conditions and applied variables. • Discussion on the benefits and application of wide-area measurement systems in the protection systems. • Discussion on balancing the security and dependability of the wide-area protection systems and comparison of some proposed methods. • Discussion on the out-of-step protection methods and comparison of the proposed schemes for the out-of-step prediction. • Investigation of the impact of integrating RESs and FACTs in power systems on the performance of protection systems, and discussion on the new trends regarding the renewable energy resources penetration and artificial intelligent tools emerging. The reliable operation of modern power systems faces numerous challenges, including wide-area disturbances, protection system misoperation, complex interconnected configurations, limited stability margins, variable renewable energy resources, and increasing energy demands. However, advancements in measurement devices, computation, and communication technologies present opportunities for constructing smart grids and implementing effective wide-area monitoring, protection, and control systems. To address these challenges, extensive research has been conducted on utilizing wide-area information to enhance protection and monitoring systems and provide a comprehensive understanding of the power system status for control centers to prevent undesirable conditions. This paper provides a comprehensive review of existing work in the areas of wide-area monitoring and protection, with a particular focus on balancing security and reliability in protection systems, supervising distance protection systems, wide-area protection against out-of-step conditions, voltage profile monitoring, and addressing issues related to integrating renewable energy resources. By examining the benefits and challenges of wide-area monitoring and protection systems in modern power systems, this review aims to identify opportunities for future research and development in this field. [ABSTRACT FROM AUTHOR]

Modern electrical power systems place special demands on the speed and accuracy of transient and steady-state process control. The introduction of renewable energy sources has significantly influenced the amount of inertia and uncertainty of transient processes occurring in energy systems. These changes have led to the need to clarify the existing principles for the implementation of devices for protecting power systems from the loss of small-signal and transient stability. Traditional methods of developing these devices do not provide the required adaptability due to the need to specify a list of accidents to be considered. Therefore, there is a clear need to develop fundamentally new devices for the emergency control of power system modes based on adaptive algorithms. This work proposes to develop emergency control methods based on the use of deep machine learning algorithms and obtained data from synchronized vector measurement devices. This approach makes it possible to ensure adaptability and high performance when choosing control actions. Recurrent neural networks, long short-term memory networks, restricted Boltzmann machines, and self-organizing maps were selected as deep learning algorithms. Testing was performed by using IEEE14, IEEE24, and IEEE39 power system models. Two data samples were considered: with and without data from synchronized vector measurement devices. The highest accuracy of classification of the control actions’ value corresponds to the long short-term memory networks algorithm: the value of the accuracy factor was 94.31% without taking into account the data from the synchronized vector measurement devices and 94.45% when considering this data. The obtained results confirm the possibility of using deep learning algorithms to build an adaptive emergency control system for power systems.

Disturbances in power systems, such as a generator trip, affect the frequency and voltage of the power grid. A framework that enables anticipating potentially dangerous frequency excursions (as signs of the disturbance start to reach sensors) is crucial to allow the operator more time to maintain or improve the electrical system’s reliability. We propose statistical models for anticipating disturbances in the frequency response at several locations simultaneously, and we quantify the risk of a frequency excursion. After probabilistic forecasts of the power grid frequency are obtained, a Bayesian decision framework is developed to issue real-time event recommendations based on actions that minimize the risk over some loss functions predefined by stakeholders. A case study with real-time monitored data from the FNET/GridEye system shows that the traditional autoregressive models underestimate the uncertainty, leading to high chances of missing a rapid frequency drop. On the other hand, this case study reveals the value of including spatial dependence in the model. The proposed spatiotemporal model better captures the uncertainty and significantly increases the probability of identifying large frequency excursions. Results show that the spatiotemporal model can significantly reduce risks for the system operator as it consistently indicates lower costs than the ones from classical models for a wide range of loss functions.

Current trends in power system development and the issues they pose have increased the urgency of automatic and emergency control alongside transient electromechanical processes. Such control devices place high demands on mode-parameter estimation algorithms in terms of their speed, accuracy, and transient sensitivity. This paper proposes a rapid estimation algorithm for power system parameters to investigate its optimal settings and applicability in both static and dynamic states. The proposed algorithm is based on signal approximation on sliding window using a multiparameter model, which is highly stable and reliable. The signals modeled in MATLAB/Simulink were used as initial data. From the experimental results, it became clear that the rapid estimation algorithm of the power system parameters showed high accuracy in steady-state mode as well as during transients, and its optimal parameters were identified. The developed algorithm can be used in relay protection, automatic and emergency control devices, and synchronous machine state-monitoring systems.

This manuscript presents a data quality analysis and holistic ‘machine learning-readiness’ evaluation of a representative set of large-scale, real-world phasor measurement unit (PMU) datasets provided under the United States Department of Energy-funded FOA 1861 research program. A major focus of this study is to understand the present-day suitability of large-scale, real-world synchrophasor datasets for application of commercially-available, off-the-shelf big data and supervised or semi-supervised machine learning (ML) tools and catalogue any major obstacles to their application. To this end, dataset quality is methodically examined through an interconnect-wide quantifications of basic bad data occurrences, a summary of several harder-to-detect data quality issues that can jeopardize successful application of machine learning, and an evaluation of the adequacy of event log labeling for supervised training of models used for online event classification. A global ‘six-point’ statistical analyses of several key dataset variables is demonstrated as a means by which to identify additional hard-to-detect data quality issues, also providing an example successful application of big data technology to extract insights regarding reasonable operational bounds of the US power system. Obstacles for application of commercial ML technologies are summarized, with a particular focus on supervised and semi-supervised ML. Lessons-learned are provided regarding challenges associated with present-day event labeling practices, large spatial scope of the dataset, and dataset anonymization. Finally, insight into efficacy of employed mitigation strategies are discussed, and recommendations for future work are made.

The combined usage of phasor measurement units and machine learning algorithms provides the opportunity for developing response-based wide-area system integrity protection scheme against transient instability in power systems. However, only the transient stability status is usually predicted in the literature, which is not enough for real-time decision-making for response-based emergency control. In this paper, an integrated approach is proposed. The GRU-based predictor is firstly proposed for post-disturbance transient stability prediction. On this basis, a multi-task learning framework is proposed for the identification of unstable machines and also the estimation of generation shedding. Case study on the IEEE 39-bus system demonstrates that, apart from the basic task of transient stability prediction, the proposed GRU-based multi-task predictor can predict the grouping of unstable machines correctly. Moreover, based on the estimated amount of generation shedding, the generated remedial control actions can retain the synchronism of the power system.

Wide-area fault location (WAFL) refers to the estimation of fault distance on the faulted line using PMU data. A system of linear equations is formulated for WAFL, taking advantage of both voltage and current synchrophasors. This results in a generalized closed-form solution for the fault distance using the weighted least-squares method. The main contribution of the letter is the rigorous derivation of the equation weights based on the statistical distributions of the superimposed errors, i.e., the differences between the errors of the corresponding pre- and post-fault synchrophasors. The method's effectiveness, robustness against different factors, and superiority over existing methods are demonstrated by extensive simulations and comparison studies conducted on the IEEE 39-bus test system. [ABSTRACT FROM AUTHOR]

The use of synchronized measurement technology leads to a more reliable and secure operation of the power system. Phasor calculation is needed for all buses where a phasor measurement unit is installed, and fast and precise estimation is necessary for accurate monitoring, protection and control. The Prony algorithm is one promising method due to its capability to estimate phasor adaptively with changing frequency. The algorithm projects the signal on $L$ exponentially damped sine functions, where $L$ is the order of the Prony. However, the order under different conditions should be adapted automatically to reduce computation time. To adaptively specify the order, the empirical mode decomposition (EMD) method is proposed in this paper to be combined with the Prony and named EMD-Prony. The EMD decomposes a signal into finite single oscillatory modes, representing the number of modes in the signal. EMD is also used as a pre-processing step to filter noise from an input signal of Prony. Therefore, EMD is proposed here as an assistant of Prony in a phasor estimation. Finally, the proposed method is tested on the benchmark signals proposed in the IEEE standard, signals obtained from a simulated power system, and measured data from a real-world power system. [ABSTRACT FROM AUTHOR]

The letter describes a novel, continuum-based approach, to capture the evolution of electromechanical dynamics in a power network following a disturbance. Such approach is based on the frequency divider formula (FDF), which was recently proposed by the third author. A key point in obtaining the frequency divider formula (FDF) as a consequence of a continuum, is to show that the spatial rate of change, at a given time, of the frequency along a lossless line is constant. The proposed derivation is then compared with the electromechanical wave approach (EWA), which has been discussed in the literature in a variety of hues and is aimed at modeling the propagation in a power system of frequency oscillations following a disturbance. The discussion illustrates similarities and differences between FDF and electromechanical wave approach (EWA). [ABSTRACT FROM AUTHOR]